# Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression

arXiv: https://arxiv.org/abs/1907.02787 · HTML: https://mlsanity.com/paper/1907.02787/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 21 of 21)

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- [TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI](https://mlsanity.com/paper/2406.12411/) (2024)
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- [Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation](https://mlsanity.com/paper/2512.09185/) (2025)
- [Individualized multi-horizon MRI trajectory prediction for Alzheimer's Disease](https://mlsanity.com/paper/2408.02018/) (2024)
- [PIE: Simulating Disease Progression via Progressive Image Editing](https://mlsanity.com/paper/2309.11745/) (2023)
- [Brain Image Synthesis with Unsupervised Multivariate Canonical CSCℓ4Net](https://mlsanity.com/paper/2103.11587/) (2021)
- [Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation](https://mlsanity.com/paper/2509.03141/) (2025)
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- [XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning](https://mlsanity.com/paper/2207.13223/) (2022)
- [4DLoG: Generative Modeling of Neurodegenerative Brain Anatomy with 4D Longitudinal Diffusion Model](https://mlsanity.com/paper/2604.22700/) (2026)
- [Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI](https://mlsanity.com/paper/2212.12810/) (2022)
- [A Lightweight Generative Model for Interpretable Subject-level Prediction](https://mlsanity.com/paper/2306.11107/) (2023)
- [Time Series Generative Learning with Application to Brain Imaging Analysis](https://mlsanity.com/paper/2407.14003/) (2024)
- [The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span](https://mlsanity.com/paper/2511.21530/) (2025)
- [3D-GlioPREDICT: 3D Latent Diffusion for Post-Radiotherapy Brain MRI Prediction in Patients with Glioma](https://mlsanity.com/paper/2606.05113/) (2026)
- [Forecasting Intrathecal Tracer Enhancement from Pre-Contrast Brain MRI: Direct Regression versus Flow Matching](https://mlsanity.com/paper/2609.22377/) (2026)

## Abstract (arXiv)

Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression. This capability is valuable for secondary prevention clinical trials where outcomes and screening criteria involve neuroimaging. Traditional computational methods are limited by imposing a parametric model for atrophy and are extremely resource-demanding. Recent advances in deep learning have yielded data-driven models for longitudinal studies (e.g., face ageing) that are capable of generating synthetic images in real-time. Similar solutions can be used to model trajectories of atrophy in the brain, although new challenges need to be addressed to ensure accurate disease progression modelling. Here we propose Degenerative Adversarial NeuroImage Net (DaniNet) --- a new deep learning approach that learns to emulate the effect of neurodegeneration on MRI by simulating atrophy as a function of ages, and disease progression. DaniNet uses an underlying set of Support Vector Regressors (SVRs) trained to capture the patterns of regional intensity changes that accompany disease progression. DaniNet produces whole output images, consisting of 2D-MRI slices that are constrained to match regional predictions from the SVRs. DaniNet is also able to maintain the unique brain morphology of individuals. Adversarial training ensures realistic brain images and smooth temporal progression. We train our model using 9652 T1-weighted (longitudinal) MRI extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We perform quantitative and qualitative evaluations on a separate test set of 1283 images (also from ADNI) demonstrating the ability of DaniNet to produce accurate and convincing synthetic images that emulate disease progression.